Triple
T3786101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cotton gin |
E85533
|
entity |
| Predicate | effectOnProduction |
P24558
|
FINISHED |
| Object | increased speed of cotton processing |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: increased speed of cotton processing | Statement: [cotton gin, effectOnProduction, increased speed of cotton processing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnProduction Context triple: [cotton gin, effectOnProduction, increased speed of cotton processing]
-
A.
increasesProductionOf
chosen
Indicates that one entity causes a rise or enhancement in the amount or rate at which another entity is produced.
-
B.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
C.
effectOnTaxes
Indicates how one entity or action changes, influences, or determines the amount, structure, or treatment of taxes for another entity or situation.
-
D.
effectOnUnitedStates
Indicates the impact, influence, or consequences that something has on the United States.
-
E.
fromProduction
Indicates that something originates from, is derived from, or is produced by a particular production process, source, or facility.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee634c6ac819099653c660c286746 |
completed | March 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69aee3d3c92c819081d9d5c45ef37a5d |
completed | March 9, 2026, 3:14 p.m. |
Created at: March 9, 2026, 3:13 p.m.